Quantifying Confidence in Assurance 2.0 Arguments
2026-04-02T08:51:56Z•a94773346b61ded445042ec0977764ed47f7342d18c601208749915336390f25
CIEA-debtFrechet-boundsLLMsRAGagent-architecturesarchitecture-metamodelassurance-2.0assurance-casesautomatic-program-repaircode-repairenergy-efficiencyenergy-footprintenterprise-architecture-debtenterprise-automationenvironmental-MLfault-localizationgenerative-AIlarge-language-modelsmicroservicesperformance-regressionpractitioner-studyprobabilistic-confidencerisk-aware-batchingterminal-agents
What happened
Collection of recent CS-SE arXiv postings (Apr 2, 2026) covering methods and empirical studies across software engineering and AI-driven systems. Key contributions include: a probabilistic method to quantify confidence in Assurance 2.0 arguments; LLM-based detection of Enterprise Architecture Debt from unstructured documentation; measurement and analysis of inference-time energy footprints for domain-specific RAG vs generic LLM workflows; advocacy and evaluation of terminal-only coding agents for enterprise automation; empirical study of how microservice topology affects performance and energy
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_se
- Record identifier
- a94773346b61ded445042ec0977764ed47f7342d18c601208749915336390f25
- Enrichment time
- 2026-04-02T08:51:56Z
- AI-assisted enrichment
- Yes
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